/////////////////////////////////////////////////////////////////////////
//
// 'ADDITION AND CONVOLUTION' RooFit tutorial macro #208
//
// One-dimensional numeric convolution
// (require ROOT to be compiled with --enable-fftw3)
//
// pdf = landau(t) (x) gauss(t)
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooLandau.h"
#include "RooFFTConvPdf.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
using namespace RooFit ;
void rf208_convolution()
{
// S e t u p c o m p o n e n t p d f s
// ---------------------------------------
// Construct observable
RooRealVar t("t","t",-10,30) ;
// Construct landau(t,ml,sl) ;
RooRealVar ml("ml","mean landau",5.,-20,20) ;
RooRealVar sl("sl","sigma landau",1,0.1,10) ;
RooLandau landau("lx","lx",t,ml,sl) ;
// Construct gauss(t,mg,sg)
RooRealVar mg("mg","mg",0) ;
RooRealVar sg("sg","sg",2,0.1,10) ;
RooGaussian gauss("gauss","gauss",t,mg,sg) ;
// C o n s t r u c t c o n v o l u t i o n p d f
// ---------------------------------------
// Set #bins to be used for FFT sampling to 10000
t.setBins(10000,"cache") ;
// Construct landau (x) gauss
RooFFTConvPdf lxg("lxg","landau (X) gauss",t,landau,gauss) ;
// S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f
// ----------------------------------------------------------------------
// Sample 1000 events in x from gxlx
RooDataSet* data = lxg.generate(t,10000) ;
// Fit gxlx to data
lxg.fitTo(*data) ;
// Plot data, landau pdf, landau (X) gauss pdf
RooPlot* frame = t.frame(Title("landau (x) gauss convolution")) ;
data->plotOn(frame) ;
lxg.plotOn(frame) ;
landau.plotOn(frame,LineStyle(kDashed)) ;
// Draw frame on canvas
new TCanvas("rf208_convolution","rf208_convolution",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}